In a previous post I explained the basics of probability. In this post I will use some of those principles to see how to solve certain problems. I will pick a very simple problem that I found in a statistics textbook. Suppose I have 7 friends who are smokers. The probability that a random smoker will develop a lung condition is 0.3. What is the probability that a maximum of 2 of them will develop a severe lung condition? To apply the binomial formula for this problem – I need the following conditions to be met:

1 The trials are independent

2 The number of trials, n is fixed.

3 Each trial outcome can be a success or a failure

4 The probability of success in each case is the same.

Applying these rules –

1 The 7 smoking friends are not related or from the same group. (This is important as one friend’s habits can influence another and that does not make for an independent trial).

2 They smoke approximately at the same rate.

3 Either they get a lung disease or they don’t. We are not considering other issues they may have because of smoking.

4 Since all these conditions are met, the probability of each of them getting a lung disease is more or less the same.

The binomial formula given that

x = total number of “successes” (pass or fail, heads or tails etc.)

P = probability of a success on an individual trial

n = number of trials

q= 1 – p – is as below:

For those not math savvy the ! stands for factorial of a number. In above example n equals 7. x, The number of ‘successes’ (morbid, i know, to define a lung condition as a success but just an example) we are looking for is 2. p is given to be 0. and q is 1 – 0.3 which is 0.7. Now, given the rules of probability – we need to add probability of 0 or none having a lung condition, 1 person having a lung condition and 2 having a lung condition – to see what is the probability of a maximum of 2 having a lung condition. Let us look at doing this with T-SQL first, then with R and then calling the R script from within T-SQL.

**1 Using T-SQL:**

There are a lot of different ways to write the simple code of calculating factorial. I found this one to be most handy and reused it. I created the user defined function as ‘factorial’ and used the same code below to calculate probabilities of 0.1 or 2 people getting a lung illness. If we add the 3 together we get the total probability of the maximum of 2 people getting a lung illness – which is about 0.65 or 65 %.

DECLARE @n decimal(10,2), @x decimal(10, 2), @p decimal(10, 2), @q decimal(10, 2) DECLARE @p0 decimal(10, 2), @p1 decimal(10, 2), @p2 decimal(10, 2), @n1 decimal(10, 2), @n2 decimal(10, 2), @n3 decimal(10, 2) SELECT @n = 7, @x = 0, @p = 0.3,@q=0.7 SELECT @x = 0 SELECT @n1 = dbo.factorial(@n) SELECT @n2 = dbo.factorial(@n-@x) SELECT @n3 = 1 SELECT @p1 = ( @n1/(@n2 * @n3))*power(@p, @x)*power(@q,@n-@x) select @p1 as 'Probability of 0 people getting lung illness' SELECT @x = 1 SELECT @p1 = ( @n/@x)*power(@p, @x)*power(@q,@n-@x) select @p1 as 'Probability of 1 person getting lung illness' SELECT @x = 2 SELECT @n1 = dbo.factorial(@n) SELECT @n2 = dbo.factorial(@n-@x) SELECT @n3 = dbo.factorial(@x) SELECT @p2 = ( @n1/(@n2 * @n3))*power(@p, @x)*power(@q,@n-@x) select @p2 as 'Probability of 2 people getting lung illness'

Results are as below:

**2 Using R:**

The R function for this is seriously simple, one line call as below.

dbinom(0:2, size=7, prob=0.3)

My results, almost exactly the same as what we got with T-SQL.

**3 Calling R from T-SQL:**

Instead of writing all that code i can simply call this function from with TSQL –

EXEC sp_execute_external_script @language = N'R' ,@script = N'x<-dbinom(0:2, size=7, prob=0.3); print(x);' ,@input_data_1 = N'SELECT 1;'

Results as below:

It is a LOT of fun to get our numbers to tie in more than one way. Thanks for reading.

Mala,

Nice article, can you please provide also the factorial that you use in your code?

Thanks,

Oded Dror

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Hi , thank you for your response..i used the code from this link http://sql-server-2012-tutorials.blogspot.com/2013/04/factorial-function-in-sql-server.html – i just called the udf ‘factorial’.

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Mala,

Thank you

Oded Dror

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Thanks for the article.

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